Practice Dimensionality Reduction: Principal Component Analysis (PCA) Introduction - 1.4.7 | Module 1: ML Fundamentals & Data Preparation | Machine Learning
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics

Grades

Grade 8 Grade 9 Grade 10 Grade 11 Grade 12

Curriculum

CBSE ICSE IB
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.

games
Typing
Memory
Math
English Adventures
Knowledge

1.4.7 - Dimensionality Reduction: Principal Component Analysis (PCA) Introduction

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the purpose of dimensionality reduction in machine learning?

💡 Hint: Think about how too many features can confuse a model.

Question 2

Easy

Define Principal Component Analysis (PCA).

💡 Hint: It's about finding directions that explain the most variation.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does PCA stand for?

  • Principal Component Analysis
  • Principal Condition Analysis
  • Primary Component Analysis

💡 Hint: It's a key method in dimensionality reduction.

Question 2

True or False: The first principal component captures the least variance of the data.

  • True
  • False

💡 Hint: Think about how variance is measured in PCA.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given a dataset with 100 features, you perform PCA and decide to keep only the top 10 principal components. Discuss how this can affect your model, both positively and negatively.

💡 Hint: Consider the balance between dimensionality reduction and information retention.

Question 2

You're tasked with applying PCA to a dataset for a classification problem. Describe how you would approach implementing PCA step-by-step, and what considerations you must take into account regarding data interpretation.

💡 Hint: Think about the sequential approach and what each step entails.

Challenge and get performance evaluation